OpenRaiser/Intern-Atlas huggingface dataset:
DiffusAL: Coupling Active Learning with Graph Diffusion for Label-Efficient Node Classification
Venue: arXiv cs.LG 2023
Year: 2023

Node classification is one of the core tasks on attributed graphs, but successful graph learning solutions require sufficiently labeled data. To keep annotation costs low, active graph learning focuses on selecting the most qualitative subset of nodes that maximizes label efficiency. However, deciding which heuristic is best suited for an unlabeled graph to increase label efficiency is a persistent challenge. Existing solutions either neglect aligning the learned model and the sampling method or focus only on limited selection aspects. They are thus sometimes worse or only equally good as random sampling. In this work, we introduce a novel active graph learning approach called DiffusAL, showing significant robustness in diverse settings. Toward better transferability between different graph structures, we combine three independent scoring functions to identify the most informative node samples for labeling in a parameter-free way: i) Model Uncertainty, ii) Diversity Component, and iii) Node Importance computed via graph diffusion heuristics. Most of our calculations for acquisition and training can be pre-processed, making DiffusAL more efficient compared to approaches combining diverse selection criteria and similarly fast as simpler heuristics. Our experiments on various benchmark datasets show that, unlike previous methods, our approach significantly outperforms random selection in 100% of all datasets and labeling budgets tested.
PATRONoC: Parallel AXI Transport Reducing Overhead for Networks-on-Chip targeting Multi-Accelerator DNN Platforms at the Edge
Venue: arXiv cs.AR 2023
Year: 2023

Emerging deep neural network (DNN) applications require high-performance multi-core hardware acceleration with large data bursts. Classical network-on-chips (NoCs) use serial packet-based protocols suffering from significant protocol translation overheads towards the endpoints. This paper proposes PATRONoC, an open-source fully AXI-compliant NoC fabric to better address the specific needs of multi-core DNN computing platforms. Evaluation of PATRONoC in a 2D-mesh topology shows 34% higher area efficiency compared to a state-of-the-art classical NoC at 1 GHz. PATRONoC's throughput outperforms a baseline NoC by 2-8X on uniform random traffic and provides a high aggregated throughput of up to 350 GiB/s on synthetic and DNN workload traffic.
Multispectral Image Segmentation in Agriculture: A Comprehensive Study on Fusion Approaches
Venue: arXiv cs.CV 2023
Year: 2023

Multispectral imagery is frequently incorporated into agricultural tasks, providing valuable support for applications such as image segmentation, crop monitoring, field robotics, and yield estimation. From an image segmentation perspective, multispectral cameras can provide rich spectral information, helping with noise reduction and feature extraction. As such, this paper concentrates on the use of fusion approaches to enhance the segmentation process in agricultural applications. More specifically, in this work, we compare different fusion approaches by combining RGB and NDVI as inputs for crop row detection, which can be useful in autonomous robots operating in the field. The inputs are used individually as well as combined at different times of the process (early and late fusion) to perform classical and DL-based semantic segmentation. In this study, two agriculture-related datasets are subjected to analysis using both deep learning (DL)-based and classical segmentation methodologies. The experiments reveal that classical segmentation methods, utilizing techniques such as edge detection and thresholding, can effectively compete with DL-based algorithms, particularly in tasks requiring precise foreground-background separation. This suggests that traditional methods retain their efficacy in certain specialized applications within the agricultural domain. Moreover, among the fusion strategies examined, late fusion emerges as the most robust approach, demonstrating superiority in adaptability and effectiveness across varying segmentation scenarios. The dataset and code is available at https://github.com/Cybonic/MISAgriculture.git.
General Anomaly Detection of Underwater Gliders Validated by Large-scale Deployment Datasets
Venue: arXiv cs.RO 2023
Year: 2023

Underwater gliders have been widely used in oceanography for a range of applications. However, unpredictable events like shark strikes or remora attachments can lead to abnormal glider behavior or even loss of the instrument. This paper employs an anomaly detection algorithm to assess operational conditions of underwater gliders in the real-world ocean environment. Prompt alerts are provided to glider pilots upon detecting any anomaly, so that they can take control of the glider to prevent further harm. The detection algorithm is applied to multiple datasets collected in real glider deployments led by the University of Georgia's Skidaway Institute of Oceanography (SkIO) and the University of South Florida (USF). In order to demonstrate the algorithm generality, the experimental evaluation is applied to four glider deployment datasets, each highlighting various anomalies happening in different scenes. Specifically, we utilize high resolution datasets only available post-recovery to perform detailed analysis of the anomaly and compare it with pilot logs. Additionally, we simulate the online detection based on the real-time subsets of data transmitted from the glider at the surfacing events. While the real-time data may not contain as much rich information as the post-recovery one, the online detection is of great importance as it allows glider pilots to monitor potential abnormal conditions in real time.
From Talent Shortage to Workforce Excellence in the CHIPS Act Era: Harnessing Industry 4.0 Paradigms for a Sustainable Future in Domestic Chip Production
Venue: arXiv cs.CY 2023
Year: 2023

The CHIPS Act is driving the U.S. towards a self-sustainable future in domestic chip production. Decades of outsourced manufacturing, assembly, testing, and packaging has diminished the workforce ecosystem, imposing major limitations on semiconductor companies racing to build new fabrication sites as part of the CHIPS Act. In response, a systemic alliance between academic institutions, the industry, government, various consortiums, and organizations has emerged to establish a pipeline to educate and onboard the next generation of talent. Establishing a stable and continuous flow of talent requires significant time investments and comes with no guarantees, particularly factoring in the low workplace desirability in current fabrication houses for U.S workforce. This paper will explore the feasibility of two paradigms of Industry 4.0, automation and Augmented Reality(AR)/Virtual Reality(VR), to complement ongoing workforce development efforts and optimize workplace desirability by catalyzing core manufacturing processes and effectively enhancing the education, onboarding, and professional realms-all with promising capabilities amid the ongoing talent shortage and trajectory towards advanced packaging.
Verifiable Data Sharing Scheme for Dynamic Multi-Owner Setting
Venue: arXiv cs.CR 2023
Year: 2023

One of scenarios in data-sharing applications is that files are managed by multiple owners, and the list of file owners may change dynamically. However, most existing solutions to this problem rely on trusted third parties and have complicated signature permission processes, resulting in additional overhead. Therefore, we propose a verifiable data-sharing scheme (VDS-DM) that can support dynamic multi-owner scenarios. We introduce a management entity that combines linear secret-sharing technology, multi-owner signature generation, and an aggregation technique to allow multi-owner file sharing. Without the help of trusted third parties, VDS-DM can update file signatures for dynamically changing file owners, which helps save communication overhead. Moreover, users independently verify the integrity of files without resorting to a third party. We analyse the security of VDS-DM through a security game. Finally, we conduct enough simulation experiments and the outcomes of experimental demonstrate the feasibility of VDS-DM.
Enhanced Security with Encrypted Vision Transformer in Federated Learning
Venue: arXiv cs.CR 2023
Year: 2023

Federated learning is a learning method for training models over multiple participants without directly sharing their raw data, and it has been expected to be a privacy protection method for training data. In contrast, attack methods have been studied to restore learning data from model information shared with clients, so enhanced security against attacks has become an urgent problem. Accordingly, in this article, we propose a novel framework of federated learning on the bases of the embedded structure of the vision transformer by using the model information encrypted with a random sequence. In image classification experiments, we verify the effectiveness of the proposed method on the CIFAR-10 dataset in terms of classification accuracy and robustness against attacks.
Smoothed Analysis of the 2-Opt Heuristic for the TSP under Gaussian Noise
Venue: arXiv cs.DS 2023
Year: 2023

The 2-opt heuristic is a very simple local search heuristic for the traveling salesperson problem. In practice it usually converges quickly to solutions within a few percentages of optimality. In contrast to this, its running-time is exponential and its approximation performance is poor in the worst case. Englert, Röglin, and Vöcking (Algorithmica, 2014) provided a smoothed analysis in the so-called one-step model in order to explain the performance of 2-opt on d-dimensional Euclidean instances, both in terms of running-time and in terms of approximation ratio. However, translating their results to the classical model of smoothed analysis, where points are perturbed by Gaussian distributions with standard deviation sigma, yields only weak bounds. We prove bounds that are polynomial in n and 1/sigma for the smoothed running-time with Gaussian perturbations. In addition, our analysis for Euclidean distances is much simpler than the existing smoothed analysis. Furthermore, we prove a smoothed approximation ratio of O(log(1/sigma)). This bound is almost tight, as we also provide a lower bound of Omega(log n/ loglog n) for sigma = O(1/sqrt n). Our main technical novelty here is that, different from existing smoothed analyses, we do not separately analyze objective values of the global and local optimum on all inputs (which only allows for a bound of O(1/sigma)), but simultaneously bound them on the same input.
Domain Adaptation based on Human Feedback for Enhancing Generative Model Denoising Abilities
Venue: arXiv cs.CV 2023
Year: 2023

How can we apply human feedback into generative model? As answer of this question, in this paper, we show the method applied on denoising problem and domain adaptation using human feedback. Deep generative models have demonstrated impressive results in image denoising. However, current image denoising models often produce inappropriate results when applied to domains different from the ones they were trained on. If there are `Good' and `Bad' result for unseen data, how to raise up quality of `Bad' result. Most methods use an approach based on generalization of model. However, these methods require target image for training or adapting unseen domain. In this paper, to adapting domain, we deal with non-target image for unseen domain, and improve specific failed image. To address this, we propose a method for fine-tuning inappropriate results generated in a different domain by utilizing human feedback. First, we train a generator to denoise images using only the noisy MNIST digit '0' images. The denoising generator trained on the source domain leads to unintended results when applied to target domain images. To achieve domain adaptation, we construct a noise-image denoising generated image data set and train a reward model predict human feedback. Finally, we fine-tune the generator on the different domain using the reward model with auxiliary loss function, aiming to transfer denoising capabilities to target domain. Our approach demonstrates the potential to efficiently fine-tune a generator trained on one domain using human feedback from another domain, thereby enhancing denoising abilities in different domains.
Artifact: Measuring and Mitigating Gaps in Structural Testing
Venue: arXiv cs.SE 2023
Year: 2023

The artifact used for evaluating the experimental results of Measuring and Mitigating Gaps in Structural Testing is publicly available on GitHub, Software Heritage and figshare, and is reusable. The artifact consists of necessary data, tools, scripts, and detailed documentation for running the experiments and reproducing the results shown in the paper. We have also provided a VirtualBox VM image allowing users to quickly setup and reproduce the results. Users are expected to be familiar using the VirtualBox software and Linux platform for evaluating or reusing the artifact.
Fine-Grained Sports, Yoga, and Dance Postures Recognition: A Benchmark Analysis
Venue: arXiv cs.CV 2023
Year: 2023

Human body-pose estimation is a complex problem in computer vision. Recent research interests have been widened specifically on the Sports, Yoga, and Dance (SYD) postures for maintaining health conditions. The SYD pose categories are regarded as a fine-grained image classification task due to the complex movement of body parts. Deep Convolutional Neural Networks (CNNs) have attained significantly improved performance in solving various human body-pose estimation problems. Though decent progress has been achieved in yoga postures recognition using deep learning techniques, fine-grained sports, and dance recognition necessitates ample research attention. However, no benchmark public image dataset with sufficient inter-class and intra-class variations is available yet to address sports and dance postures classification. To solve this limitation, we have proposed two image datasets, one for 102 sport categories and another for 12 dance styles. Two public datasets, Yoga-82 which contains 82 classes and Yoga-107 represents 107 classes are collected for yoga postures. These four SYD datasets are experimented with the proposed deep model, SYD-Net, which integrates a patch-based attention (PbA) mechanism on top of standard backbone CNNs. The PbA module leverages the self-attention mechanism that learns contextual information from a set of uniform and multi-scale patches and emphasizes discriminative features to understand the semantic correlation among patches. Moreover, random erasing data augmentation is applied to improve performance. The proposed SYD-Net has achieved state-of-the-art accuracy on Yoga-82 using five base CNNs. SYD-Net's accuracy on other datasets is remarkable, implying its efficiency. Our Sports-102 and Dance-12 datasets are publicly available at https://sites.google.com/view/syd-net/home.
Kidnapping Deep Learning-based Multirotors using Optimized Flying Adversarial Patches
Venue: arXiv cs.RO 2023
Year: 2023

Autonomous flying robots, such as multirotors, often rely on deep learning models that make predictions based on a camera image, e.g. for pose estimation. These models can predict surprising results if applied to input images outside the training domain. This fault can be exploited by adversarial attacks, for example, by computing small images, so-called adversarial patches, that can be placed in the environment to manipulate the neural network's prediction. We introduce flying adversarial patches, where multiple images are mounted on at least one other flying robot and therefore can be placed anywhere in the field of view of a victim multirotor. By introducing the attacker robots, the system is extended to an adversarial multi-robot system. For an effective attack, we compare three methods that simultaneously optimize multiple adversarial patches and their position in the input image. We show that our methods scale well with the number of adversarial patches. Moreover, we demonstrate physical flights with two robots, where we employ a novel attack policy that uses the computed adversarial patches to kidnap a robot that was supposed to follow a human.
Learning Green's Function Efficiently Using Low-Rank Approximations
Venue: arXiv cs.LG 2023
Year: 2023

Learning the Green's function using deep learning models enables to solve different classes of partial differential equations. A practical limitation of using deep learning for the Green's function is the repeated computationally expensive Monte-Carlo integral approximations. We propose to learn the Green's function by low-rank decomposition, which results in a novel architecture to remove redundant computations by separate learning with domain data for evaluation and Monte-Carlo samples for integral approximation. Using experiments we show that the proposed method improves computational time compared to MOD-Net while achieving comparable accuracy compared to both PINNs and MOD-Net.
Conditionally Optimal Parallel Coloring of Forests
Venue: arXiv cs.DC 2023
Year: 2023

We show the first conditionally optimal deterministic algorithm for $3$-coloring forests in the low-space massively parallel computation (MPC) model. Our algorithm runs in $O(\log \log n)$ rounds and uses optimal global space. The best previous algorithm requires $4$ colors [Ghaffari, Grunau, Jin, DISC'20] and is randomized, while our algorithm are inherently deterministic. Our main technical contribution is an $O(\log \log n)$-round algorithm to compute a partition of the forest into $O(\log n)$ ordered layers such that every node has at most two neighbors in the same or higher layers. Similar decompositions are often used in the area and we believe that this result is of independent interest. Our results also immediately yield conditionally optimal deterministic algorithms for maximal independent set and maximal matching for forests, matching the state of the art [Giliberti, Fischer, Grunau, SPAA'23]. In contrast to their solution, our algorithms are not based on derandomization, and are arguably simpler.
On MSRD codes, h-designs and disjoint maximum scattered linear sets
Venue: arXiv cs.IT 2023
Year: 2023

In this paper we study geometric aspects of codes in the sum-rank metric. We establish the geometric description of generalised weights, and analyse the Delsarte and geometric dual operations. We establish a correspondence between maximum sum-rank distance codes and h-designs, extending the well-known correspondence between MDS codes and arcs in projective spaces and between MRD codes and h-scatttered subspaces. We use the geometric setting to construct new h-designs and new MSRD codes via new families of pairwise disjoint maximum scattered linear sets.
Side-Contact Representations with Convex Polygons in 3D: New Results for Complete Bipartite Graphs
Venue: arXiv cs.CG 2023
Year: 2023

A polyhedral surface~$\mathcal{C}$ in $\mathbb{R}^3$ with convex polygons as faces is a side-contact representation of a graph~$G$ if there is a bijection between the vertices of $G$ and the faces of~$\mathcal{C}$ such that the polygons of adjacent vertices are exactly the polygons sharing an entire common side in~$\mathcal{C}$. We show that $K_{3,8}$ has a side-contact representation but $K_{3,250}$ has not. The latter result implies that the number of edges of a graph with side-contact representation and $n$ vertices is bounded by $O(n^{5/3})$.
Counterfactual Graph Transformer for Traffic Flow Prediction
Venue: arXiv cs.LG 2023
Year: 2023

Traffic flow prediction (TFP) is a fundamental problem of the Intelligent Transportation System (ITS), as it models the latent spatial-temporal dependency of traffic flow for potential congestion prediction. Recent graph-based models with multiple kinds of attention mechanisms have achieved promising performance. However, existing methods for traffic flow prediction tend to inherit the bias pattern from the dataset and lack interpretability. To this end, we propose a Counterfactual Graph Transformer (CGT) model with an instance-level explainer (e.g., finding the important subgraphs) specifically designed for TFP. We design a perturbation mask generator over input sensor features at the time dimension and the graph structure on the graph transformer module to obtain spatial and temporal counterfactual explanations. By searching the optimal perturbation masks on the input data feature and graph structures, we can obtain the concise and dominant data or graph edge links for the subsequent TFP task. After re-training the utilized graph transformer model after counterfactual perturbation, we can obtain improved and interpretable traffic flow prediction. Extensive results on three real-world public datasets show that CGT can produce reliable explanations and is promising for traffic flow prediction.
A Survey of Time Series Anomaly Detection Methods in the AIOps Domain
Venue: arXiv cs.LG 2023
Year: 2023

Internet-based services have seen remarkable success, generating vast amounts of monitored key performance indicators (KPIs) as univariate or multivariate time series. Monitoring and analyzing these time series are crucial for researchers, service operators, and on-call engineers to detect outliers or anomalies indicating service failures or significant events. Numerous advanced anomaly detection methods have emerged to address availability and performance issues. This review offers a comprehensive overview of time series anomaly detection in Artificial Intelligence for IT operations (AIOps), which uses AI capabilities to automate and optimize operational workflows. Additionally, it explores future directions for real-world and next-generation time-series anomaly detection based on recent advancements.
A Cyber-Physical Routing Protocol Exploiting Trajectory Dynamics for Mission-Oriented Flying Ad Hoc Networks
Venue: arXiv cs.NI 2023
Year: 2023

As a special type of mobile ad hoc network (MANET), the flying ad hoc network (FANET) has the potential to enable a variety of emerging applications in both civilian wireless communications (e.g., 5G and 6G) and the defense industry. The routing protocol plays a pivotal role in FANET. However, when designing the routing protocol for FANET, it is conventionally assumed that the aerial nodes move randomly. This is clearly inappropriate for a mission-oriented FANET (MO-FANET), in which the aerial nodes typically move toward a given destination from given departure point(s), possibly along a roughly deterministic flight path while maintaining a well-established formation, in order to carry out certain missions. In this paper, a novel cyber-physical routing protocol exploiting the particular mobility pattern of an MO-FANET is proposed based on cross-disciplinary integration, which makes full use of the mission-determined trajectory dynamics to construct the time sequence of rejoining and separating, as well as the adjacency matrix for each node, as prior information. Compared with the existing representative routing protocols used in FANETs, our protocol achieves a higher packet-delivery ratio (PDR) at the cost of even lower overhead and lower average end-to-end latency, while maintaining a reasonably moderate and stable network jitter, as demonstrated by extensive ns-3-based simulations assuming realistic configurations in an MO-FANET.